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Optimal assignment of resources to strengthen the weakest link in an uncertain environment

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Abstract

The weakest link principle applies to many real-life situations where a system is as productive as its bottleneck. The purpose of this paper is to show how the efficiency of a manufacturing or service process (i.e., the strength of a chain) may be maximized by optimal allocation of resources to improve the performance of the bottleneck (i.e., strengthen the weakest link) in an uncertain environment. Specifically, we consider two different versions of the stochastic bottleneck assignment problem (SBAP), which is a variation of the classic assignment problem (AP), with respective goals of minimizing the expected longest processing time and maximizing the expected lowest production rate. It is proven that each of the two intrinsically difficult SBAPs is reducible to an efficiently solvable AP provided that the processing times or the production rates are independent random variables following some families of probability distributions.

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References

  • Ahsanullah, M., & Nevzorov, V. B. (2001). Ordered random variables. Huntington: Nova.

    Google Scholar 

  • Balakrishnan, N., & Nevzorov, V. B. (2003). A primer on statistical distributions. Hoboken: Wiley-Interscience.

    Book  Google Scholar 

  • Barlevy, G. (2003). Estimating models of on-the-job search using record statistics (Working Paper No. 10146). National Bureau of Economic Research, Cambridge, MA.

  • Bertsekas, D. P. (1988). The auction algorithm: A distributed relaxation method for the assignment problem. Annals of Operation Research, 14, 105–123.

    Article  Google Scholar 

  • Brogan, W. L. (1989). Algorithm for ranked assignments with applications to multiobject tracking. Journal of Guidance, 12, 357–364.

    Article  Google Scholar 

  • Burkard, R. E., & Çela, E. (1999). Linear assignment problems and extensions. In D.-Z. Du & P. M. Pardalos (Eds.), Handbook of combinatorial optimization: Supplement volume A (pp. 75–149). Dordrecht: Kluwer.

    Google Scholar 

  • Cachon, G. (2002). Supply chain coordination with contracts (Working paper). Wharton School of Business, University of Pennsylvania, Philadelphia, PA, 1–122.

  • Carnahan, B. J., Maghsoodloo, S., Flynn, E. A., & Barker, K. N. (2006). Geometric probability distribution for modeling of error risk during prescription dispensing. American Journal of Health-System Pharmacy, 63, 1056–1061.

    Article  Google Scholar 

  • Carpaneto, G., Martello, S., & Toth, P. (1988). Algorithms and codes for the assignment problem. Annals of Operation Research, 13, 193–223.

    Article  Google Scholar 

  • Carraresi, P., & Gallo, G. (1984). Network models for vehicle and crew scheduling. European Journal of Operational Research, 16, 139–151.

    Article  Google Scholar 

  • Cohen, A. (2003). Asymmetric information and learning: Evidence from the automobile insurance market. (Harvard Law and Economics Discussion Paper No. 371). Harvard University, Cambridge, MA.

  • Dantzig, G. B. (1951). Application of the simplex method to a transportation problem. In T. C. Koopmans (Ed.), Activity analysis of production and allocation (pp. 359–373). New York: Wiley.

    Google Scholar 

  • Dantzig, G. B. (1963). Linear programming and extensions. Princeton: Princeton University Press.

    Google Scholar 

  • David, H. A., & Nagaraja, H. N. (2003). Order statistics (3rd ed.). Hoboken: Wiley-Interscience.

    Book  Google Scholar 

  • De, P., Ghosh, J. B., & Wells, C. E. (1992). On the solution of a stochastic bottleneck assignment problem and its variation. Naval Research Logistics, 39, 389–397.

    Article  Google Scholar 

  • Degan, A. (2004). A dynamic model of voting (PIER Working Paper 04-015). Penn Institute for Economic Research, University of Pennsylvania, Philadelphia, PA.

  • Dwyer, P. S. (1955). The solution of the Hitchcock transportation problem with a method of reduced matrices. Statistical Laboratory, University of Michigan, Ann Arbor, MI.

  • Easterfield, T. E. (1946). A combinatorial algorithm. Journal of the London Mathematical Society, 21, 219–226.

    Article  Google Scholar 

  • Ewashko, T. A., & Dudding, R. C. (1971). Application of Kuhn’s Hungarian assignment algorithm to posting servicemen. Operations Research, 19, 991.

    Article  Google Scholar 

  • Flood, M. M. (1953). On the Hitchcock distribution problem. Pacific Journal of Mathematics, 3, 369–386.

    Google Scholar 

  • Huang, J.-C., & Palmquist, R. B. (2001). Environmental conditions, reservation prices, and time on the market for housing. Journal of Real Estate Finance and Economics, 22, 203–219.

    Article  Google Scholar 

  • Jiang, Z., & Leung, V. C. M. (2003). A predictive demand assignment multiple access protocol for Internet access over broadband satellite networks. International Journal of Satellite Communications, 21, 451–467.

    Google Scholar 

  • Johnson, N. L., Kotz, S., & Kemp, A. W. (1992). Univariate discrete distributions (2nd ed.). New York: Wiley.

    Google Scholar 

  • Johnson, N. L., Kotz, S., & Balakrishnan, N. (1994). Continuous univariate distributions (Vol. 1, 2nd ed.). New York: Wiley.

    Google Scholar 

  • Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions. (Vol. 2, 2nd ed.). New York: Wiley.

    Google Scholar 

  • Khandani, A. K. (1998). Design of the turbo-code interleaver using Hungarian method. Electronics Letters, 34, 63–65.

    Article  Google Scholar 

  • Knuth, D. E. (1993). The Stanford GraphBase: A platform for combinatorial computing. Reading: Addison-Wesley.

    Google Scholar 

  • Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2, 83–97.

    Article  Google Scholar 

  • Kuruoğlu, E. E., & Zerubia, J. (2004). Modeling SAR images with a generalization of the Rayleigh distribution. IEEE Transactions on Image Processing, 13, 527–533.

    Article  Google Scholar 

  • Lee, D. A., Hogue, M. R., & Gallagher, M. A. (1997). Determining a budget profile from a R&D cost estimate. Journal of Cost Analysis, Fall, 29–41.

    Google Scholar 

  • Margolin, B. H., & Winokur, H. S. Jr. (1967). Exact moments of the order statistics of the geometric distribution and their relation to inverse sampling reliability of redundant systems. Journal of the American Statistical Association, 62, 915–925.

    Article  Google Scholar 

  • Miller, D. J., & Liu, W.-H. (2006). Improved estimation of portfolio value-at-risk under copula models with mixed marginals. The Journal of Futures Markets, 26, 997–1018.

    Article  Google Scholar 

  • Moriarty, P. J., Holley, W. E., & Butterfield, S. (2002). Effect of turbulence variation on extreme loads prediction for wind turbines. Journal of Solar Energy Engineering, 124, 387–395.

    Article  Google Scholar 

  • Pin, C., García de Fernando, G. D., & Ordóñez, J. A. (2002). Effect of modified atmosphere composition on the metabolism of glucose by Brochothrix thermosphacta. Applied and Environmental Microbiology, 68, 4441–4447.

    Article  Google Scholar 

  • Ranjan, P., Xie, M., & Goh, T. N. (2003). Optimal control limits for CCC charts in the presence of inspection errors. Quality and Reliability Engineering International, 19, 149–160.

    Article  Google Scholar 

  • Schrijver, A. (2005). On the history of combinatorial optimization (till 1960). In K. Aardal, G. L. Nemhauser, & R. Weismantel (Eds.), Handbooks in operations research and management science: Vol. 12. Discrete optimization. Amsterdam: North-Holland. (Chap. 1).

    Chapter  Google Scholar 

  • Taylor, W. R. (2002). Protein structure comparison using bipartite graph matching and its application to protein structure classification. Molecular & Cellular Proteomics, 1, 334–339.

    Article  Google Scholar 

  • Tidström, L. (2004). Estimation of probabilities of detection for cracks in pipes in Swedish nuclear power plants (Project Report 2004:2). Department of Mathematics, Uppsala University, Uppsala, Sweden.

  • Trivedi, K. S. (2002). Probability and statistics with reliability, queuing and computer science applications (2nd ed.). New York: Wiley.

    Google Scholar 

  • van Dorp, J. R., & Kotz, S. (2002). The standard two-sided power distribution and its properties: With applications in financial engineering. The American Statistician, 56, 90–99.

    Article  Google Scholar 

  • Votaw, D. F., & Orden, A. (1952). The personnel assignment problem. In A. Orden & L. Goldstein (Eds.), Symposium on linear inequalities and programming (pp. 155–163). Project SCOOP, U.S. Air Force Headquarters, Washington, DC.

    Google Scholar 

  • Yechiali, U. (1968). A stochastic bottleneck assignment. Management Science, 14, 732–734.

    Article  Google Scholar 

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Correspondence to Ching-Chung Kuo.

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Kuo, CC. Optimal assignment of resources to strengthen the weakest link in an uncertain environment. Ann Oper Res 186, 159–173 (2011). https://doi.org/10.1007/s10479-011-0860-0

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